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Early Teen Marriage and Future Poverty

Early Teen Marriage and Future Poverty. By Gordon Dahl Demography, 2010. Central Question. How does marriage as a teenager affect the likelihood of being in poverty later in life? Is the relationship between early marriage and poverty causal?

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Early Teen Marriage and Future Poverty

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  1. Early Teen Marriage andFuture Poverty By Gordon Dahl Demography, 2010

  2. Central Question How does marriage as a teenager affect the likelihood of being in poverty later in life? Is the relationship between early marriage and poverty causal? Take care to account for education and work opportunities/requirements.

  3. Early Marriage and Dropout • Early marriage associated with divorce, high fertility, dropping out, low rates of college completion. • Dropping out associated with low wages, unemployment, bad health, and crime. • Effects spillover to children and others • Interesting question: Is this “rational” behavior? • Discount rate “too high” • Time-inconsistent preferences • Projection bias

  4. Early Marriage and Dropout Question: Why might early marriage have a (large) negative causal effect?

  5. Data • 1960, 1970, and 1980 Census • Used to create a data set of women born 1920-1954. • Marriage license data • National Fertility Surveys • Compiled a data set on state marriage, schooling, and labor laws.

  6. Figure 1

  7. Data & Methods Figure 2 emphasizes what we already know from this class—lots going on from 1940-1960 with marriage, divorce, and fertility. What does this mean for the estimation? Will need to properly account for trends and different effects by time period. Also affects interpretation.

  8. Data & Methods OLS Specification

  9. Data & Methods OLS Specification Yist = α + β1*EMist + β2*DOist + Xist*β3 + γs + τ+ t*Rs + εist Yist = 1 if woman’s family is below poverty line

  10. Data & Methods OLS Specification Yist = α + β1*EMist + β2*DOist + Xist*β3 + γs + τ+ t*Rs + εist Yist = 1 if woman’s family is below poverty line EMist = 1 if early marriage (before 16)

  11. Data & Methods OLS Specification Yist = α + β1*EMist + β2*DOist + Xist*β3 + γs + τ+ t*Rs + εist Yist = 1 if woman’s family is below poverty line EMist = 1 if early marriage (before 16) DOist = 1 if dropout

  12. Data & Methods OLS Specification Yist = α + β1*EMist + β2*DOist + Xist*β3 + γs + τ+ t*Rs + εist Yist = 1 if woman’s family is below poverty line EMist = 1 if early marriage (before 16) DOist = 1 if dropout Xist = individual characteristics, incl. race, age, year

  13. Data & Methods OLS Specification Yist = α + β1*EMist + β2*DOist + Xist*β3 + γs + τ+ t*Rs + εist Yist = 1 if woman’s family is below poverty line EMist = 1 if early marriage (before 16) DOist = 1 if dropout Xist = individual characteristics, incl. race, age, year γs + τ + t*Rs: state- and year-of-birth dummies, region trends

  14. Table 1

  15. Table 1 (cont’d) When collapsing data to cells, Dahl finds larger effects. He suggests that the individual-level analysis is subject to measurement error, which would result in attenuation bias. Attenuation bias: if an independent variable is measured with (random) error, the estimated coefficient is biased toward zero.

  16. State Marriage, Schooling, and Labor Laws IV can deal with both OVB and measurement error. Use state laws restricting age at marriage as an instrument. * Describe the natural experiment.

  17. State Marriage, Schooling, and Labor Laws Do the laws satisfy our two conditions? 1. Are marriage laws correlated with likelihood of early marriage?

  18. State Marriage, Schooling, and Labor Laws

  19. State Marriage, Schooling, and Labor Laws Do the laws satisfy our two conditions? 2. Are marriage laws uncorrelated with error term? Possibly not, if laws are endogenous. For example, could be some omitted characteristic that is driving both the law change and the changes in early marriage. Can’t test, but shows that future laws do not predict today’s early marriage rate.

  20. Results Interpret coefficients in 1st column: Marrying before age 16 increases the likelihood of poverty by 31 percentage points, c.p. Dropping out increases likelihood of poverty by 11 percentage points, c.p.

  21. Results: Alternative Specifications 1st column: Results very similar when using grouped data. Suggests have taken care of measurement error. 2nd column: Valid if instruments are “weak” (laws weakly correlated with early marriage). 3rd column: Accounts for fact that some women may have moved and therefore we have incorrectly assigned their legal environment.

  22. Other Results Table 6: Effect of early marriage stronger for Blacks, more precisely estimated for South Table 8: Women frequently misreport their date of birth and date of marriage!

  23. Discussion Do you believe the results (magnitude)? Policy implications? Also: Can we separate the effects of early marriage from early fertility? Does the effect of early marriage change over lifetime (get worse or better)? Why doesn’t Dahl consider more recent cohorts?

  24. Discussions on Research Question What is your research question? * Make sure it’s a question! * Not too broad * Identify the key outcome (dependent variable) What would the ideal experiment look like? What would the ideal data look like? What are the major endogeneity issues for your question?

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